Text Classification
Transformers
PyTorch
English
bert
finance
sentiment analysis
regression
sentence bert
text-embeddings-inference
Instructions to use LHF/FinEAS with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LHF/FinEAS with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LHF/FinEAS")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LHF/FinEAS") model = AutoModelForSequenceClassification.from_pretrained("LHF/FinEAS") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 348dbba7812075932e75b8d8473c571c53ac4a4f15e77ea42bc202c1fb7d9677
- Size of remote file:
- 438 MB
- SHA256:
- 8f2832642ea606384c88a72cc552ac2369c7decb5440fa6f28b617a90ec2d065
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